No abstract

The ground validation of satellite-based precipitation products (SPPs) is very important for their hydroclimatic application. This study evaluated the performance assessment of four soil moisture-based SPPs (SM2Rain, SM2Rain- ASCAT, SM2Rain-CCI, and GPM-SM2Rain). All data of SPPs were compared with 64 weather stations in Pakistan from January 2005 to December 2020. All SPPs estimations were evaluated on daily, monthly, seasonal, and yearly scales, over the whole spatial domain, and at point-to-pixel scale. Widely used evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (rBias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI) were evaluated for performance analysis. The results of our study signposted that: (1) On a monthly scale, all SPPs estimations were in better agreement with gauge estimations as compared to daily scales. Moreover, SM2Rain and GPM-SM2Rain products accurately traced the spatio-temporal variability with CC >0.7 and rBIAS within the acceptable range (±10) of the whole country. (2) On a seasonal scale (spring, summer, winter, and autumn), GPM-SM2Rain performed more satisfactorily as compared to all other SPPs. (3) All SPPs performed better at capturing light precipitation events, as indicated by the Probability Density Function (PDF); however, in the summer season, all SPPs displayed considerable over/underestimates with respect to PDF (%). Moreover, GPM-SM2RAIN beat all other SPPs in terms of probability of detection. Consequently, we suggest the daily and monthly use of GPM-SM2Rain and SM2Rain for hydro climate applications in a semi-arid climate zone (Pakistan).

The outbreak of the covid-19 pandemic has devastated many sectors of each country and led to the development of contact tracing applications for controlling its spread. Contact tracing apps have been promoted to track infected contacts. However, contact tracing has gained significant debate due to its security and privacy concerns. The goal of this study is to examine the most popular contact tracing apps, their impact on pandemic control, as well security and privacy concerns. The multivocal literature review (MLR) brings the results from the state-of-the-art literature. We extracted 23 studies from both formal and grey literature to achieve the research objectives and found several security and privacy threats in the existing contact tracing applications. Additionally, the best practices to address these threats were also identified. We further proposed a preliminary structure of a secure global contact tracing app using blockchain technology.

With the dramatic evolution in networks nowadays, an equivalent growth of challenges has been depicted toward implementing and deployment of such networks. One of the serious challenges is the security where wide range of attacks would threat these networks. Denial-of-Service (DoS) is one of the common attacks that targets several types of networks in which a huge amount of information is being flooded into a specific server for the purpose of turning of such server. Many research studies have examined the simulation of networks in order to observe the behavior of DoS. However, the variety of its types hinders the process of configuring the DoS attacks. In particular, the Distributed DoS (DDoS) is considered to be the most challenging threat to various networks. Hence, this paper aims to accommodate a comprehensive simulation in order to figure out and detect DDoS attacks. Using the well-known simulator technique of NS-2, the experiments showed that different types of DDoS have been characterized, examined and detected. This implies the efficacy of the comprehensive simulation proposed by this study.